Abstract
We present a reproducibility study of the state-of-the-art neural architecture for sequence labeling proposed by Ma and Hovy (2016)\cite{ma2016end}. The original BiLSTM-CNN-CRF model combines character-level representations via Convolutional Neural Networks (CNNs), word-level context modeling through Bi-directional Long Short-Term Memory networks (BiLSTMs), and structured prediction using Conditional Random Fields (CRFs). This end-to-end approach eliminates the need for hand-crafted features while achieving excellent performance on named entity recognition (NER) and part-of-speech (POS) tagging tasks. Our implementation successfully reproduces the key results, achieving 91.18\% F1-score on CoNLL-2003 NER and demonstrating the model's effectiveness across sequence labeling tasks. We provide a detailed analysis of the architecture components and release an open-source PyTorch implementation to facilitate further research.
Abstract (translated)
我们对Ma和Hovy（2016）\cite{ma2016end}提出的最新序列标注神经架构进行了可重复性研究。原BiLSTM-CNN-CRF模型结合了通过卷积神经网络（CNNs）产生的字符级表示，双向长短期记忆网络（BiLSTMs）进行的词级别上下文建模以及使用条件随机场（CRFs）实现的结构化预测。这种端到端的方法消除了手工特征设计的需求，并在命名实体识别（NER）和词性标注（POS）任务上取得了优异的成绩。 我们的实现在关键结果方面成功地再现了原始研究，具体而言，在CoNLL-2003 NER数据集上实现了91.18%的F1分数，展示了该模型在序列标注任务中的有效性。我们提供了对架构组件的详细分析，并发布了一个开源的PyTorch实现，以促进进一步的研究。
URL
https://arxiv.org/abs/2510.10936
